Location: Southwest Watershed Research CenterTitle: Curating 62 years of Walnut Gulch Experimental Watershed data: Improving the quality of long-term rainfall and runoff datasets
|DEMARIA, E.M.C. - University Of Arizona|
|Heilman, Philip - Phil|
|Goodrich, David - Dave|
|WEI, H. - University Of Arizona|
|THIYAGARAJA PERUMAL, A. - University Of Arizona|
Submitted to: Water
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/30/2022
Publication Date: 7/12/2022
Citation: Meles, M.B., Demaria, E., Heilman, P., Goodrich, D.C., Kautz, M.A., Armendariz, G.A., Unkrich, C.L., Wei, H., Thiyagaraja Perumal, A. 2022. Curating 62 years of Walnut Gulch Experimental Watershed data: Improving the quality of long-term rainfall and runoff datasets. Water. 14(14). Article 2198. https://doi.org/10.3390/w14142198.
Interpretive Summary: Long-term records of rainfall and runoff are crucial to understanding hydrologic processes within a watershed or landscape and predicting how these interactions will respond to future climatic, land use, and biophysical changes. Located in the semi-arid Southwestern U.S., the Walnut Gulch Experimental Watershed (WGEW) has professionally allocated efforts since 1953 to measure rainfall and runoff. These efforts have resulted in high quality long-term datasets which have improved our understanding of watershed hydrology in semiarid environments. Despite several improvements in data collection and storage over the years, undetected errors still exist within these datasets. Instrument malfunctions and human error in data collection contribute to errors in these data. To identify these errors and inconsistencies, we utilized 5 different hydrologic methods to develop a suite of quality control tools. Each of these methods was developed based on common hydrological principles and their relationships, such as the similarity of precipitation recordings in neighboring rain gauges; the temporal relationship between precipitation and runoff; known relationships between the amount of runoff generated from an associated rainfall event; and that extreme events require closer scrutiny. These semi-automated data quality checking tools were developed and applied in WGEW to improve the quality of the dataset. The implementation of the methods described in this work have detected several previously undetected errors in the data archive, providing researchers with more accurate data and means for improved hydrologic analyses and model predictions.
Technical Abstract: The Walnut Gulch Experimental Watershed (WGEW) is the primary outdoor hydrologic laboratory for the USDA-ARS’ Southwest Watershed Research Center (SWRC). This site represents the semiarid environment of the Southwest U.S. within the Long-term Agroecosystem Research (LTAR) network. The SWRC maintains a collection of long-term hydro-climatic measurements from WGEW, featuring an extensive archive of rainfall and runoff observations from an ephemeral network of streams within the 149 km2 watershed. The WGEW was established in 1953, and has continually developed and improved quality assurance and quality control (QAGC) procedures to aid in the accuracy and curation of the constantly growing datasets obtained from more than 100 rain gauges and 18 flumes, weirs, and gauged ponds. These efforts have led to the development of a state-of-the-art database and data visualization tools to aid in the curation of research grade hydrometeorologic datasets. This required development of automated quality assurance and quality control (QAQC) tools to check and maintain the data for 21st century research needs. To improve the integrity of the historical rainfall and runoff database, five independent tools were developed to identify questionable events based on the following conventional hydrologic principles and the relationships between them: 1) precipitation is spatially correlated; 2) there is a temporal relation between rainfall and runoff; 3) there is an upper limit to the amount of runoff generated from a given rainfall event; and, 4) it is always important to verify extreme events. Hence, we developed and applied the following methods that included the analysis of interpolated rainfall maps at a daily time step, the association between paired rainfall and runoff events, the computation of runoff lag time, runoff coefficients, and the analysis of multiple regression methods to identify problematic events in the data archive. To visually inspect and verify the errors, we developed a graphical tool that displays relevant event hyetographs and hydrographs within a specific window of time. After flagging anomalous events, we evaluated the types of errors using the information in the original records and metadata. The implementation of these approaches resulted in the development of a suite of semi-automated QAQC tools that correctly detected 813 rainfall and 24 runoff events with erroneous timestamps that had passed all previous quality checks.